System and method of conjugate adaptive conjugate masking empirical mode decomposition

ABSTRACT

The invention discloses a method and a system for quickly and directly processing an original signal into a plurality of mode functions, the steps comprises: decomposing the original signal by Empirical Mode Decomposition (EMD) method to choose a first intrinsic mode functions (IMFs). Then, adding the plurality of level n conjugate masking functions, which are selected from a group of sinusoidal functions comprising the mean amplitude and the mean frequency of the first IMF, to the original signal individually to obtain level n mode functions, until the level n mode function is a monotonic function, wherein the plurality of mode functions are the IMFs between each frequency regions of the original signal. The invention not only includes the advantage of EMD analyzing, but also excludes the problem of mode mixing phenomenon which is caused by the intermittent disturbance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Non-provisional application claims priority under 35 U.S.C. §119(a)on Patent Application No(s). [201510688238.2] filed in China [Oct. 22,2015], the entire contents of which are hereby incorporated byreference.

FIELD OF THE INVENTION

The invention relates to a method and system for low computation costand no noise containment to decompose the nonlinear and non-stationarysignal. More particularly, to a method and system of conjugate adaptiveConjugate Masking Empirical Mode Decomposition (CADM EMD) based ondyadic decomposition.

BACKGROUND OF THE INVENTION

The Empirical Mode Decomposition (EMD) method based on an algorithm ofHilbert-Huang Transformation (HHT) is a versatile method for analyzingnonlinear and non-stationary data. The EMD method has been widelyapplied to many fields that including ocean waves, geophysics, climatechanges, infrastructural health monitoring, machine vibrations andstability, biomedical sciences.

During EMD decomposing, the original signal based on high-frequency tolow-frequency with corresponding functions in the form of the IntrinsicMode Functions (IMFs). However, one of the main drawbacks of EMD is modemixing. The mode mixing, which is the phenomenon of having oscillationsof different scales residing in one IMF, thus it is impossible to assignclear physical meaning to that particular IMF component. The mode mixingphenomenon is the consequence of the intermittency in data resultingfrom the complex physical processes.

The Ensemble Empirical Mode Decomposition (EEMD) is presented (Wu andHuang, 2009). The key idea on the EEMD relies on averaging the modesobtained by EMD applied to several realizations of Gaussian white noiseadded to the original signal. The resulting solves the EMD mode mixingproblem. However, the drawback of this statistically sound approach ismainly the computation cost. To get statistically significant meanwithout noticeable residual noise, the usual implement of the EEMD hadto include more than 100 trials that means a hundred times slower thanthe EMD. Another drawback is the computation cost, which increase withthe number of variables involved. Under this condition, the computationcost may be too high to be worthy of practical applications.

SUMMARY OF THE INVENTION

The present invention provides to a method and a system of conjugateadaptive dyadic masking empirical mode decomposition (CADM EMD). CADMEMD not only includes the advantage of Empirical Mode Decomposition(EMD) analyzing, but also excludes the problem of mode mixing phenomenonwhich is caused by the intermittent disturbance.

In an embodiment of the invention, the present invention provides amethod implemented in computer for processing an original signal into aplurality of mode functions, the method comprising: receiving theoriginal signal; decomposing the original signal by EMD method togenerate a plurality of intrinsic mode functions (IMFs); choosing afirst IMF, then meaning each instant frequency and each instantamplitude of the first IMF to determine a mean frequency and a meanamplitude, wherein the first IMF is determined by a highest frequencyregion of the plurality of IMFs.

And adding a plurality of level one conjugate masking functionsindividually to the original signal to generate a plurality of level onemodify signals, wherein the plurality of level one conjugate maskingfunctions with same phase difference are selected from a group ofsinusoidal functions comprising the mean amplitude and the meanfrequency. The method further obtains a plurality of level one modifyIMFs by EMD method to decompose the plurality of level one modifysignals; summing the plurality of level one modify IMFs, and dividing bya number of level one conjugate masking function to obtain a level onemode function.

And adding a plurality of level two conjugate masking functionsindividually to the original signal to obtain a plurality of level twomodify signals, wherein the plurality of level two conjugate maskingfunctions with same phase difference are selected from a group ofsinusoidal functions comprising the mean amplitude and the meanfrequency; The method further obtains a plurality of level two modifyIMFs by EMD method to decompose the plurality of level two modifysignals: summing the plurality of level two modify IMFs, and dividing bya number of level two conjugate masking function to obtain a level twomode function.

Repeating steps above, adding a plurality of level n conjugate maskingfunctions to the original signal individually to obtain a level n modefunctions, until the level n mode function is a monotonic function,wherein the plurality of level n conjugate masking functions with samephase difference are selected from a group of sinusoidal functionscomprising the mean amplitude and the mean frequency.

Finally, constructing the level one mode function to the level n modefunction, wherein the mode functions are the IMFs between each frequencyregion of the original signal.

In another embodiment of the invention, the signal processing systemcomprises an input device, a computing device, and an output device.

The input device receives an original signal.

The computing device comprises a decomposing processor and an analyzingprocessor. The decomposing processor is connected to the input devicefor decomposing the original signal by EMD method to generate aplurality of IMFs, and choosing a first IMF, meaning each instantfrequency and each instant amplitude of the first IMF to determine amean frequency and a mean amplitude, wherein the first IMF is determinedby a highest frequency region of the plurality of IMFs.

The analyzing processor is connected to the decomposing processor foradding a plurality of level one conjugate masking functions individuallyto the original signal to generate a plurality of level one modifysignals, wherein the plurality of level one conjugate masking functionswith same phase difference are selected from a group of sinusoidalfunctions comprising the mean amplitude and the mean frequency; thenobtaining a plurality of level one IMFs by EMD method to decompose theplurality of level one modify signals; summing the plurality of levelone modify IMFs, and dividing by a number of level one conjugate maskingfunction to obtain a level one mode function.

Then adding a plurality of level two conjugate masking functionsindividually to the original signal to obtain a plurality of level twomodify signals, wherein the plurality of level two conjugate maskingfunctions with same phase difference are selected from a group offollowing sinusoidal functions comprising the mean amplitude and themean frequency; then obtaining a plurality of level two modify IMFs byEMD method to decompose the plurality of level two modify signals:summing the plurality of level two modify IMFs, and dividing by a numberof level two conjugate masking function to obtain a level two modefunction.

Furthermore, repeating steps above, adding a plurality of level nconjugate masking functions to the original signal individually toobtain a level n mode function, until the level n mode function is amonotonic function, wherein the level n conjugate masking functions withsame phase difference are selected from a group of sinusoidal functionscomprising the mean amplitude and the mean frequency.

The output device is connected to the analyzing processor forconstructing the level one mode function to the level n mode function,wherein the mode functions are the IMFs between each frequency region ofthe original signal.

Other systems, methods, features, and advantages of the presentdisclosure will be or become apparent to one with skill in the art uponexamination of the following drawings and detailed description. It isintended that all such additional systems, methods, features, andadvantages be included within this description, be within the scope ofthe present disclosure, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with referenceto the following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views. The patent or application file contains atleast one drawing executed in color. Copies of this patent or patentapplication publication with color drawing(s) will be provided by theOffice upon request and payment of the necessary fee.

FIG. 1 is a block diagram of a system implemented in computer forprocessing an original signal into a plurality of mode functions.

FIGS. 2A and 2B illustrates the intrinsic mode functions (IMFs)decomposed by Empirical Mode Decomposition (EMD) and Ensemble EmpiricalMode Decomposition (EEMD) in the prior art.

FIG. 3A and FIG. 3B illustrates the IMFs decomposed by ConjugateAdaptive Dyadic Masking EMD (CADM EMD) with phase difference π/4 (4masking) and π/8 (8 masking) in the present disclosure.

FIG. 4A, illustrates the IMFs of a blood pressure signal decomposed byEEMD in the prior art.

FIG. 4B illustrates the intrinsic mode functions of a blood pressuresignal decomposed by CADM EMD in the present disclosure.

FIG. 5 is a steps flowchart of a method implemented in computer forprocessing an original signal into a plurality of mode functions.

DETAILED DESCRIPTION OF THE INVENTION

Having summarized various aspects of the present disclosure, referencewill now be made in detail to the description of the disclosure asillustrated in the drawings. While the disclosure will be described inconnection with these drawings, there is no intent to limit it to theembodiment or embodiments disclosed herein. On the contrary, the intentis to cover all alternatives, modifications and equivalents includedwithin the spirit and scope of the disclosure as defined by the appendedclaims.

In an embodiment of the invention, the present invention discloses amethod of conjugate adaptive conjugate masking empirical modedecomposition (CADM EMD) implemented in a signal analysis system duringdyadic decomposition of nonlinear and non-stationary data in a lowcomputation cost. It is understood that the method provides merely anexample of the many different types of functional arraignments that maybe employed to implement the operation of the various components of asystem, for example, a computer system connected to a scanner, amultiprocessor computing device, and so forth. The execution steps ofthe present invention may include application specific software whichmay store in any portion or component of the memory including, forexample, random access memory (RAM), read-only memory (ROM), hard drive,solid-state drive, magneto optical (MO), IC chip, USB flash drive,memory card, optical disc such as compact disc (CD) or digital versatiledisc (DVD), or other memory components.

For some embodiments, the system comprises an input device, a computingdevice, and an output device. The input device used to receive data suchas image, text or control signals and provide to a computing device suchas a computer or other information appliance. In accordance with someembodiments, the computing device includes a storage medium and aplurality of processors. The storage medium is such as, by way ofexample and without limitation, a hard drive, an optical device or aremote database server coupled to a network, and stores softwareprograms. The processors perform data calculations, data comparisons,and data copying. The output device that visually conveys text,graphics, and intrinsic mode function (IMF). Information shown on theoutput device is called soft copy because the information existselectronically and is displayed for a temporary period of time. Theoutput device includes CRT monitors, LCD monitors and displays, gasplasma monitors, and televisions.

In accordance with such embodiments of present invention, the softwareprograms are stored in the storage medium and executed by the processorswhen the computing device executes the method of CADM EMD. Finally,information provided by the computing device, and presented on theoutput device or stored in the storage medium.

Please refer FIG. 1, it is understood that the method of conjugateadaptive dyadic masking EMD provides merely an example of the manydifferent types of functional arrangements that may be employed toimplement the operation of the various components of the signalprocessing system 100. The signal processing system 100 comprises aninput device 110, a computing device 120 and an output device 130,wherein the computing device 120 comprises a decomposing processor 122,and an analyzing processor 124.

The input device 110 receives an input signal such as an originalsignal, wherein the original signal is a nonlinear and unstable signal(data). In another embodiment, the input device 110 may receive theoriginal signal wirelessly.

The decomposing processor 122 is connected to the input device 110decomposes the original signal by using an EMD method to generate aplurality of IMFs. The computing device 120 chooses a first IMF, meaningeach instant frequency and each instant amplitude of the first IMF todetermine a mean frequency (ω_(o)) and a mean amplitude (a_(o)), whereinthe first IMF is determined by a highest frequency region of theplurality of IMFs.

The analyzing processor 124 is connected to the decomposing processor122 for adding a plurality of level one conjugate masking functionsindividually to the original signal to generate a plurality of level onemodify signals, wherein the plurality of level one conjugate maskingfunctions with same phase difference are selected from a group offollowing sinusoidal functions comprising the mean amplitude and themean frequency. And the analyzing processor 124 decomposes the level onemodify signals by using the EMD method to obtain a plurality of levelone modify IMFs.

In one embodiment, the level one conjugate masking functions areseparated by phase difference of τ/2, wherein the level one conjugatemasking function is computed based on the following formula:

+a_(o) sin ω_(o)t, −a_(o) sin ω_(o)t, +a_(o) cos ω_(o)t and −a_(o) cosω_(o)t

In embodiment, the level one conjugate masking functions are separatedby phase difference of π/4, wherein the level one conjugate maskingfunction is computed based on the following formula:

+a_(o) sin ω_(o)t, −a_(o) sin ω_(o)t, +a_(o) cos ω_(o)t, −a_(o) cosω_(o)t,

+a_(o) sin(ω_(o)t+π/4), −a_(o) sin(ω_(o)t+π/4), +a_(o) cos(ω_(o)t+π/4)and −a_(o) cos(ω_(o)t+π/4)

The level one conjugate masking function such as, by way of example andwithout limitation, π/8 or π/16 could be added as required by specialsituation, which will make the result even smoother, wherein thesinusoidal function further comprises (ω_(o)t+π/4) and (ω_(o)t+π/8).However, the computing time of the analyzing processor 124 is increasingwhen the phase difference is smaller. In an advantageous embodiment, theanalyzing processor 124 performs a lower computation cost which hasbetter performance than EMD and the EEMD when the phase difference isπ/4. The level one modify IMFs of the invention are to make moreaccurate and guarantee conformation to the white noise characteristics.

The analyzing processor 124 further calculates the sum of the pluralityof level one modify IMFs, and be divided by a number of level oneconjugate masking function to obtain a level one mode function forelimination increasing progressively with level one modify IMFs, whereinthe effects of the masking function will cancel out due to their pairedconjugate properties of the masking functions, for example, sin and cos.

The analyzing processor 124 adds a plurality of level two conjugatemasking functions individually to the original signal to obtain aplurality of level two modify signals, wherein the plurality of leveltwo conjugate masking functions with same phase difference are selectedfrom a group of sinusoidal functions comprising the mean amplitude(a_(o)) and the mean frequency (ω_(o)). And the analyzing processor 124decomposes the plurality of level two modify signals by EMD method toobtain a plurality of level two modify IMFs.

In one embodiment, the level two conjugate masking functions areseparated by phase difference of π/2, wherein the level two conjugatemasking function is computed based on the following formula:

+(a_(o)/2)sin(ω_(o)/2)t,−(a_(o)/2)sin(ω_(o)/2)t,

+(a_(o)/2)cos(ω_(o)/2)t and −(a_(o)/2)cos(ω_(o)/2)t

In one embodiment, the level two conjugate masking functions areseparated by phase difference of π/4, wherein the level two conjugatemasking function is computed based on the following formula:

+(a_(o)/2)sin(ω_(o)/2)t, −(a_(o)/2)sin(ω_(o)/2)t,

+(a_(o)/2)cos(ω_(o)/2)t,−(a_(o)/2)cos(ω_(o)/2)t,

+(a_(o)/2)sin(ω_(o)/2+π/4)t, −(a_(o)/2)sin(ω_(o)/2+π/4)t,

+(a_(o)/2)cos(ω_(o)/2+π/4)t and −(a_(o)/2)cos(ω_(o)/2+π/4)t

The conjugate masking function such as, by way of example and withoutlimitation, π/8 or π/16 could be added as required by special situation,which will make the result even smoother, wherein the sinusoidalfunction further comprises (ω_(o)t+π/4) and (ω_(o)t+π/8).

The analyzing processor 124 further calculates the sum of the pluralityof level two IMFs, and be divided by a number of level two conjugatefunction to obtain a level two mode function. The level two conjugatemasking functions with same phase difference are selected from a groupof sinusoidal functions, for example, the mean amplitude (a_(o)) and themean frequency (ω_(o)).

In one embodiment, the level two conjugate masking functions areseparated by phase difference of π/2. The analyzing processor 124performs steps above repeatedly for adding a plurality of level nconjugate masking functions to the original signal individually toobtain a level n mode function, until the level n mode function is amonotonic function from which no more mode function can be extracted.The plurality of level n conjugate masking functions with same phasedifference are selected from a group of sinusoidal functions, forexample, the mean amplitude (a_(o)) and the mean frequency (ω_(o)).

In one embodiment, the plurality of level n conjugate masking functionsare separated by phase difference of π/2, wherein the level n conjugatemasking function is computed based on the following formula:

+(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,−(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,

+(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t and−(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t

In one embodiment, the plurality of level n conjugate masking functionsare separated by phase difference of π/4, wherein the level n conjugatemasking function is computed based on the following formula:

+(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,−(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,

+(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t,−(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t,

+(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1)+π/4)t,−(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1)+π/4)t,

+(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1)+π/4)t and−(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1)+π/4)t,

The conjugate masking function such as, by way of example and withoutlimitation, π/8 or π/16 could be added as required by special situation,which will make the result even smoother, wherein the sinusoidalfunction further comprises (ω_(o)/2^(n-1)+π/4) and (ω_(o)/2^(n-1)+π/8).

The output device 130 is connected to the analyzing processor 124 forconstructing the level one mode function to level n mode function,wherein the mode functions are the IMFs between each frequency regionsof the original signal.

The output device 130 is visually conveys text, graphics, and thesignal. Information shown on the output device 130 is called soft copybecause the information exists electronically and is displayed for atemporary period of time. The output device 130 is connected to thesystem 100 including a touch screen device, a printer device, CRTmonitors, LCD monitors and displays, gas plasma monitors, andtelevisions. For example, the touch screen device receives the nonlinearand non-stationary signal as an input signal from the system 100, forexample, a computer or a signal analysis device. The nonlinear andnon-stationary signal is decomposed by the EMD method to generate theIMFs. The IMF is displayed on the touch screen such as cardiogram or animage for user analysis and observation.

FIG. 2A and FIG. 2B illustrate the IMFs decomposed by EMD and EEMD inthe prior art. FIG. 3A and FIG. 3B illustrate the IMFs decomposed byCADM EMD with phase difference π/4 (4 masking) and π/8 (8 masking) inthe present disclosure. In FIG. 3A, the IMFs are generated based on theconjugate masking functions separated by the phase difference of π/2. InFIG. 3B, the IMFs are generated based on the conjugate masking functionsseparated by the phase difference of π/4. In FIGS. 2A, 2B, 3A, 3B, 4Aand 4B illustrate the frequency regions 210, 211, 212, 214, 215, 216,310, 311, 312, 214, 315, 316, 410, 412, 420, 422 are IMFs decomposed byEMD, EEMD or CADM EMD.

Reference is made to FIGS. 2A, 2B. 3A and 3B, the higher frequencyregions 210, 214, 310, 314 are compared. As a result, the IMFs of theprior arts and the invention reflect uniformity characteristics withsubtle difference frequencies and amplitudes. Further, the lowerfrequency regions 211, 215, 311, 315 are compared. As a result, the IMFsare generated by EMD. EEMD and CADM EMD which reflect differentcharacteristics.

The invention eliminates inconsistent characteristic in the IMF to solvemode mixing problem. Furthermore, the lowest frequency regions 212, 216,312, 316 are compared. As a result, the white noise is eliminated in thelowest IMFs based on CADM EMD method to solve mode mixing problem forinfluence frequencies and amplitudes. Therefore, the invention isadaptive, direct and noise free dyadic filter bank that produces cleanIMFs for further analysis. The CADM EMD performs much better with nonoticeable mode mixing in any IMF component. Furthermore, the scales ineach IMF become more uniformly distributed. All the better results areobtained with much lower computational cost.

FIG. 4A, illustrates the IMFs of a blood pressure signal decomposed byEEMD in the prior art. FIG. 4B illustrates the IMFs of a blood pressuresignal decomposed by CADM EMD in the present disclosure. Reference ismade to FIG. 4A, the IMF 410 is decomposed by EEMD in the frequencyregion −1500 to −2000. The IMF 410 is mixed with notice signal,therefore a component of a similar scale residing in the IMF 410 and theIMF 420. The IMF 420 is decomposed by the CADM EMD which results showedregulation and complete of the IMF 420 without white notice influence.

With further reference to FIG. 5, FIG. 5 is a flowchart of exemplarysteps of a method of CADM EMD in accordance with various embodiments ofthe present disclosure. An original signal, for example, nonlinear andunstable signal (data) is obtained. Beginning with step 510, theoriginal signal is decomposed by the EMD method to obtain the IMFs.

In step 520, the decomposing processor 122 chooses a first IMF, meaningeach instant frequency and each instant amplitude of the first IMF todetermine a mean frequency (ω_(o)) and a mean amplitude (a_(o)), whereinthe first IMF is determined by a highest frequency region of theplurality of IMFs.

In step 530, the analyzing processor 124 adds the paired regular sinewaves with mean amplitude (a_(o)) and mean frequency (ω_(o)) with samephase difference as conjugate masking functions to the original signalindividual to generate a plurality of level one modify signals. Theconjugate masking functions with finer phase distribution is implementedbased on the following formula:

+a_(o) sin ω_(o)t, −a_(o) sin ω_(o)t, +_(a) Cos ω_(o)t and −a_(o) cosω_(o)t

In step 540, the analyzing processor 124 obtains a plurality of levelone modify IMFs by using the EMD method to decompose the plurality oflevel one modify signals.

In step 550, the analyzing processor 124 sums the plurality of level onemodify IMFs, and be divided by a number of level one conjugate maskingfunction, to obtain a level one mode function.

In step 560, the analyzing processor 124 adds a plurality of level twoconjugate masking functions individually to the original signal toobtain a plurality of level two modify signals. The level two conjugatemasking functions with finer phase distribution is implemented based onthe following formula:

+(ao/2)sin(ω/2)t, −(ao/2)sin(ωo/2)t,

+(ao/2)cos(ωo/2)t and −(ao/2)cos(ωo/2)t

In step 570, the analyzing processor 124 decomposes the plurality oflevel two modify signals by using the EMD method to obtain a pluralityof level two IMFs.

In step 580, the analyzing processor 124 sums the plurality of level twoIMFs, and be divided by a number of level two conjugate function toobtain a level two mode function.

In step 590, the analyzing processor 124 performs step 570 to 590repeatedly. The analyzing processor 124 adds a plurality of level nconjugate masking functions individually to the original signal toobtain level n mode functions, until the level n mode function is amonotonic function from which no more mode function can be extracted.The level one conjugate masking function is computed based on thefollowing formula:

+(a_(o)2^(n-1))sin(ω_(o)/2^(n-1))t, −(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,

+(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t

−(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t

Finally, the constructor unit 140 records the level one mode function tolevel n mode function, wherein the mode functions are the IMFs betweeneach frequency regions of the original signal.

Although the present invention has been described in terms of specificexemplary embodiments and examples, it will be appreciated that theembodiments disclosed herein are for illustrative purposes only andvarious modifications and alterations might be made by those skilled inthe art without departing from the spirit and scope of the invention asset forth in the following claims.

What is claimed is:
 1. A method implemented in computer for processingan original signal into a plurality of mode functions, the methodcomprising: (A) receiving the original signal; (B) decomposing theoriginal signal by Empirical Mode Decomposition (EMD) method to generatea plurality of intrinsic mode functions (IMFs); (C) choosing a firstIMF, then meaning each instant frequency and each instant amplitude ofthe first IMF to determine a mean frequency (ω_(o)) and a mean amplitude(a_(o)), wherein the first IMF is determined by a highest frequencyregion of the plurality of IMFs; (D) adding a plurality of level oneconjugate masking functions individually to the original signal togenerate a plurality of level one modify signals, wherein the pluralityof level one conjugate masking functions with same phase difference areselected from a group of following sinusoidal functions comprising themean amplitude (a_(o)) and the mean frequency (ω_(o)):+a_(o) sin ω_(o)t, −a_(o) sin ω_(o)t, +a_(o) cos ω_(o)t and −a_(o) cosω_(o)t (E) obtaining a plurality of level one modify IMFs by using EMDmethod to decompose the plurality of level one modify signals; (F)summing the plurality of level one modify IMFs, and dividing by a numberof level one conjugate masking function to obtain a level one modefunction; (G) adding a plurality of level two conjugate maskingfunctions individually to the original signal to obtain a plurality oflevel two modify signals, wherein the plurality of level two conjugatemasking functions with same phase difference are selected from a groupof following sinusoidal functions comprising the mean amplitude (a_(o))and the mean frequency (ω_(o));+(a_(o)/2)sin(ω_(o)/2)t, −(a_(o)/2)sin(ω_(o)/2)t,+(a_(o)/2)cos(ω_(o)/2)t and −(a_(o)/2)cos(ω_(o)/2)t (H) obtaining aplurality of level two modify IMFs by using EMD method to decompose theplurality of level two modify signals; (I) summing the plurality oflevel two modify IMFs, and dividing by a number of level two conjugatemasking function to obtain a level two mode function; (J) repeating step(H) to (I), adding a plurality of level n conjugate masking functions tothe original signal individually to obtain a level n mode function,until the level n mode function is a monotonic function, wherein theplurality of level n conjugate masking functions with same phasedifference are selected from a group of following sinusoidal functionscomprising the mean amplitude (a_(o)) and the mean frequency (ω_(o));and+(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,−(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,+(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t and−(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t (K) constructing the level one modefunction to the level n mode function, wherein the mode functions arethe IMFs between each frequency region of the original signal.
 2. Themethod according to claim 1, wherein the plurality of frequencies andthe plurality of amplitudes are determined by a section which is at thehighest frequency oscillations of the first IMF.
 3. The method accordingto claim 1, wherein the conjugate masking functions is separated by thephase difference of π/2.
 4. The method according to claim 1, wherein theconjugate masking functions is separated by the phase difference of π/4.5. A system for processing an original signal into a plurality of modefunctions, comprising: an input device, receiving the original signal: acomputing device, comprising: a decomposing processor connected to theinput device for decomposing the original signal by Empirical ModeDecomposition (EMD) method to generate a plurality of intrinsic modefunctions (IMFs), and choosing a first IMF, meaning each instantfrequency and each instant amplitude of the first IMF to determine amean frequency (ω_(o)) and a mean amplitude (a_(o)), wherein the firstIMF is determined by a highest frequency region of the plurality ofIMFs; and an analyzing processor connected to the decomposing processorfor adding a plurality of level one conjugate masking functionsindividually to the original signal to generate a plurality of level onemodify signals, wherein the plurality of level one conjugate maskingfunctions with same phase difference are selected from a group offollowing sinusoidal functions comprising the mean amplitude (a_(o)) andthe mean frequency (ω_(o)),+a_(o) sin ω_(o)t, −a_(o) sin ω_(o)t, +a_(o) cos ω_(o)t and −a_(o) cosω_(o)t then obtaining a plurality of level one modify IMFs by using EMDmethod to decompose the plurality of level one modify signals, summingthe plurality of level one modify IMFs, and dividing by a number oflevel one conjugate masking function to obtain a level one modefunction, then adding a plurality of level two conjugate maskingfunctions individually to the original signal to obtain a plurality oflevel two modify signals, wherein the plurality of level two conjugatemasking functions with same phase difference are selected from a groupof following sinusoidal functions comprising the mean amplitude (a_(o))and the mean frequency (ω_(o)),+(a_(o)/2)sin(ω_(o)/2)t,−(a_(o)/2)sin(ω_(o)/2)t,+(a_(o)/2)cos(ω_(o)/2)t and −(a_(o)/2)cos(ω_(o)/2)t then obtaining aplurality of level two modify IMFs by using EMD method to decompose theplurality of level two modify signals, summing the plurality of leveltwo IMFs, and dividing by a number of level two conjugate maskingfunction to obtain a level two mode function, furthermore repeatingsteps above, adding a plurality of level n conjugate masking functionsto the original signal individually to obtain a level n mode function,until the level n mode function is a monotonic function, wherein thelevel n conjugate masking functions with same phase difference areselected from a group of following sinusoidal functions comprising themean amplitude (a_(o)) and the mean frequency (ω_(o)); and+(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,−(a_(o)/2^(n-1))sin(ω_(o)/2^(n-1))t,+(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t and−(a_(o)/2^(n-1))cos(ω_(o)/2^(n-1))t an output device connected to theanalyzing processor for constructing the level one mode function to thelevel n mode function, wherein the mode functions are the IMFs betweeneach frequency region of the original signal.
 6. The system of claim 5,wherein the processing processor is further to determine the pluralityof frequencies and the plurality of amplitudes according to a sectionwhich is at the highest frequency oscillations of the first IMF.
 7. Thesystem of claim 5, wherein the conjugate masking functions is separatedby the phase difference of π/2.
 8. The system of claim 5, wherein theconjugate masking functions is separated by the phase difference of π/4.